| Autor: |
Maldonado R; University of Texas at Dallas, Richardson, TX, USA., Yetisgen M; University of Washington, Seattle, WA, USA., Harabagiu SM; University of Texas at Dallas, Richardson, TX, USA. |
| Jazyk: |
angličtina |
| Zdroj: |
AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2019 May 06; Vol. 2019, pp. 543-552. Date of Electronic Publication: 2019 May 06 (Print Publication: 2019). |
| Abstrakt: |
Incorporating the knowledge encoded in the Unified Medical Language System (UMLS) in deep learning methods requires learning knowledge embeddings from the knowledge graphs available in UMLS: the Metathesaurus and the Semantic Network. In this paper we present a technique using Generative Adversarial Networks (GANs) for learning UMLS embeddings and showcase their usage in a clinical prediction model. When the UMLS embeddings are available, the predictions improve by up to 6.9% absolute F 1 score. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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